Apply function row in pandas data block on numerical value columns

Question

I have the following data frame:

import pandas as pd df = pd.DataFrame({'AAA' : ['w','x','y','z'], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]}) 

Which looks like this:

 In [32]: df Out[32]: AAA BBB CCC 0 w 10 100 1 x 20 50 2 y 30 -30 3 z 40 -50 

What I want to do is to execute a function function for each row for each column, except for those that have a non-numerical value (in this case, AAA ). In the real case, the odd case is always in the first column, and the rest (may be more than 2 columns) are always numerical.

The final desired result:

  AAA BBB CCC Score 0 w 10 100 110 1 x 20 50 70 2 y 30 -30 0 3 z 40 -50 -10 

I tried this but could not:

 import numpy as np df["Score"] = df.apply(np.sum, axis=1) 

What is the right way to do this?

Update2:

This is the code that SettingWithCopyWarning gives. Please get a fresh start to ipython for testing.

 import pandas as pd import numpy as np def cvscore(fclist): sd = np.std(fclist) mean = np.mean(fclist) cv = sd/mean return cv def calc_cvscore_on_df(df): df["CV"] = df.iloc[:,1:].apply(cvscore, axis=1) return df df3 = pd.DataFrame(np.random.randn(1000, 3), columns=['a', 'b', 'c']) calc_cvscore_on_df(df3[["a","b"]]) 

Answer 1

To select everything except the first column, you can use df.iloc[:, 1:] :

 In [371]: df['Score'] = df.iloc[:, 1:].sum(axis=1) In [372]: df Out[372]: AAA BBB CCC Score 0 w 10 100 110 1 x 20 50 70 2 y 30 -30 0 3 z 40 -50 -10 

To apply an arbitrary func function to each line:

 df.iloc[:, 1:].apply(func, axis=1) 

---

For instance,

 import numpy as np import pandas as pd def cvscore(fclist): sd = np.std(fclist) mean = np.mean(fclist) cv = sd/mean return cv df = pd.DataFrame({'AAA' : ['w','x','y','z'], 'BBB' : [10,20,30,40], 'CCC' : [100,50,-30,-50]}) df['Score'] = df.iloc[:, 1:].apply(cvscore, axis=1) print(df) 

gives

  AAA BBB CCC Score 0 w 10 100 1.211386 1 x 20 50 0.868377 2 y 30 -30 NaN 3 z 40 -50 -5.809058